Facts show that uncertainty-based methods have made good progress in the practice of various downstream tasks.
For example, Yarin et al. [1]} were the first to explore the nature of uncertainty in regression and classification tasks extensively, expounded that uncertainty is indispensable, and developed the necessary tools.
In semi-supervised learning, UPS [2]} proposes an uncertainty-aware pseudo-label selection framework to increase the accuracy of pseudo-labeling by reducing the noise during training.
UAFS [3]} proposes a framework based on few-shot image classification, which converts the observed similarity of query-support pairs into probabilistic representations and uses graph convolutional networks (GCN) to take advantage of this uncertainty to achieve more effective optimization results.
TIM [4]} introduces the concept of mutual information to measure the uncertainty of model prediction results and optimize the model based on the uncertainty of similarity.
